Disease classification | Number | Percent |
control | 77 | 41.0 |
RA | 55 | 29.3 |
SLE | 56 | 29.8 |
Comparison | # Up | % Up | # Down | % Down | Mean count | # Low | % Low |
RA vs control | 1690 | 4.35 | 1886 | 4.85 | 2 | 18285 | 47.01 |
SLE vs control | 2248 | 5.78 | 2377 | 6.11 | 2 | 17538 | 45.09 |
## RA_vs_control
Gene
Log2-fold change
Log-fold change
standard error
P-value*
Adjusted
P-value†
HSPA1B
7.5
0.42
6.3e-75
6.5e-71
HSPA6
7
0.50
1.0e-55
5.2e-52
HSPA1A
6.8
0.39
1.8e-79
3.6e-75
AL671762.1
6.2
0.39
4.1e-61
2.8e-57
GJB2
5.2
1.20
6.5e-06
1.8e-04
CCL22
5
0.77
6.8e-13
1.3e-10
MIR210HG
4.5
0.88
6.1e-15
2.0e-12
HSPB1
3.6
0.29
3.5e-40
1.2e-36
LINC00656
3.4
1.00
7.5e-04
8.2e-03
SNORD14E
3.1
0.51
3.0e-11
4.2e-09
THBS1
3.1
0.38
5.6e-19
3.4e-16
AC016168.2
3
0.95
1.8e-04
2.7e-03
CREM
3
0.25
1.5e-32
3.9e-29
ERRFI1
3
0.46
9.6e-14
2.3e-11
HSPH1
2.9
0.27
1.1e-30
2.3e-27
AC105105.1
2.8
0.72
1.7e-06
5.6e-05
DNAJB1
2.8
0.20
2.6e-46
1.1e-42
LIF
2.8
0.57
3.0e-09
2.3e-07
SLC6A8
2.7
0.44
2.7e-12
4.8e-10
PHLDB1
2.5
0.55
5.2e-08
2.8e-06
ADM
2.4
0.38
3.1e-12
5.2e-10
DNAH17
2.4
0.60
1.3e-07
6.1e-06
YPEL4
2.4
0.43
7.1e-11
8.6e-09
ATF3
2.3
0.24
2.4e-23
2.5e-20
NGFR
2.3
0.69
8.0e-06
2.1e-04
*Wald test p-values
†Benjamini–Hochberg adjusted value
SLE_vs_control
Gene
Log2-fold change
Log-fold change
standard error
P-value*
Adjusted
P-value†
HSPA1B
7.1
0.41
7.1e-67
7.6e-63
HSPA1A
6.6
0.39
9.7e-76
2.1e-71
TFPI2
6.1
3.00
4.9e-08
2.4e-06
CCL22
4.8
0.78
2.0e-12
3.5e-10
MIR210HG
4.7
0.80
4.7e-16
1.7e-13
AC011498.4
4.6
0.94
5.0e-11
6.3e-09
AP000941.1
4.5
1.40
1.5e-04
1.8e-03
SEMA6B
4.3
0.55
3.7e-17
1.7e-14
CBARP
4.2
1.00
5.9e-06
1.3e-04
LINC02029
4
1.20
2.1e-04
2.3e-03
HCG20
3.6
0.96
1.0e-04
1.3e-03
AC112496.1
3.5
0.85
1.3e-04
1.5e-03
HILPDA
3.5
0.75
1.9e-13
4.2e-11
SERPINB2
3.3
0.65
2.0e-09
1.6e-07
THBS1
3.3
0.41
9.1e-21
6.5e-18
OLR1
3.2
0.62
6.1e-10
5.7e-08
ABHD17AP6
3.1
1.10
2.4e-03
1.6e-02
CXCL5
3.1
1.20
3.1e-07
1.1e-05
HSPH1
3.1
0.26
7.1e-35
2.2e-31
NRG1
3
0.63
5.9e-10
5.5e-08
SNORD14E
3
0.51
7.9e-11
9.3e-09
HSPB1
2.9
0.29
4.6e-26
7.6e-23
TRPV3
2.9
0.66
2.3e-08
1.3e-06
DNAJB1
2.8
0.20
9.4e-46
4.0e-42
GRIN3B
2.8
0.73
7.2e-07
2.3e-05
*Wald test p-values
†Benjamini–Hochberg adjusted value
## RA_vs_control
Gene
Log2-fold change
Log-fold change
standard error
P-value*
Adjusted
P-value†
ACVRL1
4.9
0.86
5.2e-12
8.2e-10
TRBV12-4
4.3
0.91
6.8e-06
1.8e-04
IGLV9-49
4.1
0.79
8.7e-10
7.8e-08
HBB
3.7
0.88
7.0e-07
2.7e-05
ARLNC1
3.5
1.30
2.2e-03
1.8e-02
IGKV1-17
3.5
0.51
3.0e-14
8.3e-12
IGKV2D-28
3.5
0.49
3.9e-16
1.5e-13
IGLV2-5
3.5
0.83
3.6e-04
4.6e-03
TMEM198
3.5
0.91
2.5e-07
1.1e-05
TNFRSF17
3.5
0.52
3.3e-13
7.3e-11
IGLV3-19
3.4
0.50
9.0e-14
2.2e-11
AL162724.1
3.3
0.82
5.7e-06
1.6e-04
IGHV2-70D
3.3
0.80
7.9e-06
2.1e-04
TRBV5-1
3.2
0.57
5.6e-11
7.0e-09
TRBV7-6
3.2
0.83
6.3e-04
7.1e-03
FAM171A1
3.1
0.64
1.3e-07
6.1e-06
FAM218A
3.1
0.93
5.0e-04
6.0e-03
LINC00402
3.1
0.67
3.4e-07
1.4e-05
LINC02033
3
0.64
4.7e-07
1.9e-05
AL392172.2
2.9
1.30
3.0e-03
2.3e-02
AP001107.9
2.9
0.60
3.5e-08
2.0e-06
IGHV1-69D
2.9
0.53
1.2e-10
1.4e-08
IGLC6
2.9
0.99
1.6e-03
1.5e-02
RPL23AP81
2.9
0.73
3.1e-05
6.5e-04
FAM238B
2.8
0.94
2.4e-04
3.3e-03
HBA1
2.8
0.82
2.5e-05
5.4e-04
IGHV4-55
2.8
0.78
1.2e-05
3.0e-04
*Wald test p-values
†Benjamini–Hochberg adjusted value
SLE_vs_control
Gene
Log2-fold change
Log-fold change
standard error
P-value*
Adjusted
P-value†
IFI27
6.1
0.69
2.3e-21
1.8e-18
ELOVL6
5
0.93
3.9e-13
8.0e-11
IGLVI-70
4.7
1.00
2.9e-06
7.3e-05
CERS3
3.6
0.84
4.6e-06
1.1e-04
IGHGP
3.6
0.61
1.0e-10
1.2e-08
OTOF
3.3
0.59
6.3e-11
7.6e-09
Z99756.1
3.2
0.91
2.7e-06
6.8e-05
LINC00487
3.1
0.76
6.8e-08
3.2e-06
RTEL1P1
3.1
0.80
1.8e-06
4.8e-05
STOX1
3
0.85
4.0e-05
6.1e-04
IFI44L
2.9
0.30
1.3e-23
1.6e-20
IGLV7-46
2.8
0.56
3.2e-09
2.3e-07
AL512353.1
2.7
0.91
1.9e-05
3.4e-04
CYP2J2
2.7
0.94
3.0e-03
1.9e-02
IGHG1
2.6
0.31
1.1e-19
7.4e-17
LY6E-DT
2.6
0.72
4.5e-06
1.0e-04
USP18
2.6
0.41
8.0e-12
1.2e-09
C11orf45
2.5
0.88
4.7e-04
4.5e-03
IGHV1-69
2.5
0.57
9.2e-08
4.1e-06
KCNG1
2.5
0.81
4.7e-05
7.1e-04
LINC02033
2.5
0.65
1.2e-05
2.3e-04
NRIR
2.5
0.51
1.4e-08
8.4e-07
RUFY4
2.5
0.49
1.9e-10
2.0e-08
SIGLEC1
2.5
0.48
6.0e-09
4.0e-07
AC009686.2
2.4
0.74
5.2e-04
4.8e-03
ACTA2-AS1
2.4
0.64
2.6e-05
4.4e-04
TLDC2
2.4
0.77
2.0e-06
5.4e-05
*Wald test p-values
†Benjamini–Hochberg adjusted value
## Warning: Removed 18286 rows containing missing values (geom_point).
## Warning: Removed 17539 rows containing missing values (geom_point).
The eigenvales for the annotated Banchereau modules were used to calculate an adjacentcy matrix, which was in turn used to calculate the gap statistic to determine the optimal k-clusters.
## `summarise()` regrouping output by 'disease_class' (override with `.groups` argument)
## ℹ No targets to load in loadd().
## Warning: Removed 1 rows containing non-finite values (stat_bracket).
## Warning: Removed 1 rows containing non-finite values (stat_bracket).
## Warning: Removed 1 rows containing non-finite values (stat_bracket).
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm): collapsing to unique 'x' values
The module eigengene scores were used to train a random forest model with repeated cross validation to predict cluster identity.
The model was trained with 75% of the dataset and tested on 25%.
Note: Subjects with LP were excluded due to the small population size.
## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5 6 7 8
## 1 5 0 0 0 0 5 0 0
## 2 0 4 0 1 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 6 1 9 0 0 0 0
## 5 0 0 0 0 0 0 2 1
## 6 1 0 0 0 0 3 1 0
## 7 0 0 0 0 0 0 1 1
## 8 0 0 0 0 0 0 4 1
##
## Overall Statistics
##
## Accuracy : 0.5
## 95% CI : (0.349, 0.651)
## No Information Rate : 0.2174
## P-Value [Acc > NIR] : 2.259e-05
##
## Kappa : 0.4039
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5 Class: 6 Class: 7 Class: 8
## Sensitivity 0.8333 0.40000 0.00000 0.9000 NA 0.37500 0.12500 0.33333
## Specificity 0.8750 0.97222 1.00000 0.8056 0.93478 0.94737 0.97368 0.90698
## Pos Pred Value 0.5000 0.80000 NaN 0.5625 NA 0.60000 0.50000 0.20000
## Neg Pred Value 0.9722 0.85366 0.97826 0.9667 NA 0.87805 0.84091 0.95122
## Prevalence 0.1304 0.21739 0.02174 0.2174 0.00000 0.17391 0.17391 0.06522
## Detection Rate 0.1087 0.08696 0.00000 0.1957 0.00000 0.06522 0.02174 0.02174
## Detection Prevalence 0.2174 0.10870 0.00000 0.3478 0.06522 0.10870 0.04348 0.10870
## Balanced Accuracy 0.8542 0.68611 0.50000 0.8528 NA 0.66118 0.54934 0.62016
The relative importance of each eigengene in classification:
## parRF variable importance
##
## variables are sorted by maximum importance across the classes
## only 20 most important variables shown (out of 22)
##
## 1 2 3 4 5 6 7 8
## MEblue 14.3693 7.82545 -0.97829 8.095 0.88383 6.71602 6.422 4.4448
## MEturquoise 8.5627 5.71845 -1.88396 14.235 0.06745 1.63373 1.897 7.3597
## MEpink 11.5824 3.81770 -0.26493 6.681 -0.07100 3.47044 2.054 0.8572
## MEgrey60 11.5603 6.03835 -0.62524 6.218 1.83138 5.78396 5.676 1.3140
## MEmidnightblue 11.2442 5.38447 0.11599 7.883 -1.72204 0.90253 8.619 6.1353
## MEbrown 9.9589 6.41273 -3.25380 10.646 0.22458 4.94336 6.648 4.9923
## MEpurple 6.7482 -2.21446 -2.26207 10.334 1.53567 1.47778 5.543 1.2884
## MEcyan 5.9730 1.99198 -2.14758 10.267 0.22573 0.08171 7.071 4.2144
## MEblack 6.5315 4.94709 -2.44302 10.115 3.34137 0.32909 1.586 -1.8805
## MEyellow 7.5887 5.58255 1.05781 9.419 0.59289 1.83879 2.558 5.0972
## MEtan 7.1845 4.15669 0.03735 7.270 0.01082 0.50122 4.931 8.3790
## MElightyellow 8.1430 2.42064 1.70996 5.354 1.69733 2.54109 6.027 3.5652
## MEred 7.7584 3.77223 -1.78087 6.651 1.01509 0.54880 6.746 5.6750
## MEdarkred 7.4979 1.85037 -2.61042 6.125 0.31279 1.61495 5.423 1.1253
## MEgreenyellow 4.1659 -0.03084 -2.64787 6.788 -0.74295 2.01727 2.987 2.8514
## MEgreen 6.1628 5.94511 -2.27799 4.344 -0.14282 -1.52697 1.140 1.0719
## MElightgreen 6.0871 -3.06843 -0.28870 2.069 -0.77411 -2.03344 2.961 1.3398
## MEdarkgreen -0.2039 1.89077 -2.10168 2.749 -0.32412 0.55297 5.400 -3.7847
## MEsalmon 5.3525 3.63849 1.01297 3.311 0.08243 -1.30895 1.571 0.4298
## MElightcyan 2.5020 2.61766 0.16440 5.170 1.37282 -0.82609 2.747 -0.2886
The module eigengene scores were used to train a random forest model with repeated cross validation to predict cluster identity.
The model was trained with 75% of the dataset and tested on 25%.
## Confusion Matrix and Statistics
##
## Reference
## Prediction control RA SLE
## control 16 2 1
## RA 2 5 8
## SLE 1 6 5
##
## Overall Statistics
##
## Accuracy : 0.5652
## 95% CI : (0.4111, 0.7107)
## No Information Rate : 0.413
## P-Value [Acc > NIR] : 0.02666
##
## Kappa : 0.3391
##
## Mcnemar's Test P-Value : 0.96269
##
## Statistics by Class:
##
## Class: control Class: RA Class: SLE
## Sensitivity 0.8421 0.3846 0.3571
## Specificity 0.8889 0.6970 0.7812
## Pos Pred Value 0.8421 0.3333 0.4167
## Neg Pred Value 0.8889 0.7419 0.7353
## Prevalence 0.4130 0.2826 0.3043
## Detection Rate 0.3478 0.1087 0.1087
## Detection Prevalence 0.4130 0.3261 0.2609
## Balanced Accuracy 0.8655 0.5408 0.5692
The relative importance of each eigengene in classification:
## parRF variable importance
##
## only 20 most important variables shown (out of 22)
##
## Overall
## MElightgreen 25.735
## MEroyalblue 5.533
## MElightcyan 5.029
## MEred 4.408
## MEdarkgreen 3.831
## MElightyellow 3.762
## MEcyan 3.433
## MEgrey60 3.403
## MEblack 3.382
## MEtan 3.289
## MEsalmon 3.213
## MEgreenyellow 3.166
## MEblue 2.976
## MEgreen 2.948
## MEdarkred 2.877
## MEmidnightblue 2.718
## MEmagenta 2.417
## MEpurple 2.357
## MEyellow 2.271
## MEbrown 2.206
The module eigengene scores were used to train a random forest model with repeated cross validation to predict cluster identity.
The model was trained with 75% of the dataset and tested on 25%.
## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5 6 7 8
## 1 6 0 0 0 0 3 0 0
## 2 0 5 0 4 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 3 0 7 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 1 0 0 0 0 4 0 0
## 7 0 0 0 0 1 0 4 2
## 8 0 0 0 0 0 0 3 2
##
## Overall Statistics
##
## Accuracy : 0.6222
## 95% CI : (0.4654, 0.7623)
## No Information Rate : 0.2444
## P-Value [Acc > NIR] : 8.541e-08
##
## Kappa : 0.5436
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5 Class: 6 Class: 7 Class: 8
## Sensitivity 0.8571 0.6250 NA 0.6364 0.00000 0.57143 0.57143 0.50000
## Specificity 0.9211 0.8919 1 0.9118 1.00000 0.97368 0.92105 0.92683
## Pos Pred Value 0.6667 0.5556 NA 0.7000 NaN 0.80000 0.57143 0.40000
## Neg Pred Value 0.9722 0.9167 NA 0.8857 0.97778 0.92500 0.92105 0.95000
## Prevalence 0.1556 0.1778 0 0.2444 0.02222 0.15556 0.15556 0.08889
## Detection Rate 0.1333 0.1111 0 0.1556 0.00000 0.08889 0.08889 0.04444
## Detection Prevalence 0.2000 0.2000 0 0.2222 0.00000 0.11111 0.15556 0.11111
## Balanced Accuracy 0.8891 0.7584 NA 0.7741 0.50000 0.77256 0.74624 0.71341
The relative importance of each eigengene in classification:
## parRF variable importance
##
## variables are sorted by maximum importance across the classes
## only 20 most important variables shown (out of 260)
##
## 1 2 3 4 5 6 7 8
## M9.50 1.3644 1.3989 2.309e-01 0.0000 7.9646 -1.4095 -2.2869 16.6677
## M5.15 14.9597 2.6251 3.377e+00 8.3911 6.1483 3.4568 9.8552 8.5809
## M8.22 1.3281 12.3497 -2.536e+00 1.2379 -1.7922 1.0010 2.2928 1.7264
## M8.54 11.0772 0.1854 6.404e-01 1.0010 0.0000 -4.1093 0.1562 0.0000
## M4.1 9.4637 9.7319 -3.584e+00 10.9203 5.1621 7.1675 7.4282 8.2874
## M8.41 8.2247 10.2774 -5.887e+00 10.3112 3.7191 6.8320 9.5280 8.4319
## M4.15 9.0549 8.7563 -1.581e+00 9.8181 4.5581 4.7102 7.7209 7.9695
## M6.13 2.5102 1.1739 2.577e+00 2.7801 1.0910 -1.9585 -1.0567 8.4132
## M8.58 8.0103 4.3608 2.869e+00 4.8290 4.0826 8.2278 3.0940 5.4960
## M5.14 3.5232 -2.8498 -1.143e+00 7.8671 1.9599 2.2626 2.2390 3.6788
## M7.17 2.7396 -4.3366 4.230e-17 7.2724 1.6691 3.2706 2.7833 2.9750
## M9.30 7.1216 -0.2774 2.898e+00 1.0010 4.0843 3.9310 5.5550 5.9115
## M6.16 0.8473 -3.9059 -1.344e+00 7.0236 0.3180 0.5205 1.3714 1.7241
## M8.71 6.9733 1.6374 1.327e+00 1.3910 4.6504 4.5508 6.8717 6.2373
## M9.24 6.2781 -3.8498 1.214e+00 5.8269 0.1690 -0.8202 1.0694 5.3447
## M9.15 1.6271 -1.9084 -1.001e+00 5.9538 1.4171 1.1688 1.7262 1.5498
## M8.56 5.6236 0.4820 -8.464e-01 1.3773 3.3963 4.7765 3.0064 4.6920
## M7.29 1.0010 0.8445 3.848e+00 2.3988 1.9396 0.9768 5.3479 4.3251
## M4.3 1.0010 5.2245 1.178e+00 0.1366 0.0000 1.0010 -0.7752 0.4845
## M6.6 3.5675 3.4052 -9.351e-01 4.8004 0.4658 3.4305 5.1912 4.1304
The module eigengene scores were used to train a random forest model with repeated cross validation to predict cluster identity.
The model was trained with 75% of the dataset and tested on 25%.
## Confusion Matrix and Statistics
##
## Reference
## Prediction control RA SLE
## control 16 3 1
## RA 2 6 7
## SLE 1 4 6
##
## Overall Statistics
##
## Accuracy : 0.6087
## 95% CI : (0.4537, 0.7491)
## No Information Rate : 0.413
## P-Value [Acc > NIR] : 0.005811
##
## Kappa : 0.403
##
## Mcnemar's Test P-Value : 0.796853
##
## Statistics by Class:
##
## Class: control Class: RA Class: SLE
## Sensitivity 0.8421 0.4615 0.4286
## Specificity 0.8519 0.7273 0.8438
## Pos Pred Value 0.8000 0.4000 0.5455
## Neg Pred Value 0.8846 0.7742 0.7714
## Prevalence 0.4130 0.2826 0.3043
## Detection Rate 0.3478 0.1304 0.1304
## Detection Prevalence 0.4348 0.3261 0.2391
## Balanced Accuracy 0.8470 0.5944 0.6362
The relative importance of each eigengene in classification:
## parRF variable importance
##
## variables are sorted by maximum importance across the classes
## only 20 most important variables shown (out of 260)
##
## control RA SLE
## M2.3 13.93671 7.46191 5.7414
## M3.4 5.22186 -2.15418 8.1796
## M8.77 3.56442 7.05577 0.1395
## M8.98 6.93188 4.85291 0.9418
## M8.10 5.70636 3.49268 1.3072
## M8.93 4.75082 0.10196 3.7531
## M5.12 3.20252 1.13290 4.7210
## M1.2 4.32166 0.27341 4.3726
## M7.1 0.13589 4.31162 -0.8437
## M8.87 0.88386 4.10724 0.7403
## M4.8 3.80243 0.12635 2.0789
## M4.11 3.75735 0.02174 1.9477
## M9.23 0.93460 -1.00056 3.7241
## M8.86 0.06736 3.57070 -1.2428
## M8.6 1.82644 -1.85412 3.4964
## M7.16 1.85245 3.34325 0.6618
## M8.110 3.30882 0.80783 -0.8009
## M6.3 3.28167 0.56511 2.3193
## M9.10 1.37740 3.23515 -1.4084
## M8.44 3.22465 -0.82291 1.4468
## ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## setting value
## version R version 4.0.0 (2020-04-24)
## os Ubuntu 18.04.4 LTS
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Etc/UTC
## date 2020-10-08
##
## ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## package * version date lib source
## abind 1.4-5 2016-07-21 [1] RSPM (R 4.0.0)
## acepack 1.4.1 2016-10-29 [1] RSPM (R 4.0.0)
## annotate 1.66.0 2020-04-27 [1] Bioconductor
## AnnotationDbi 1.50.3 2020-07-25 [1] Bioconductor
## assertthat 0.2.1 2019-03-21 [1] RSPM (R 4.0.0)
## backports 1.1.7 2020-05-13 [1] RSPM (R 4.0.0)
## base64enc 0.1-3 2015-07-28 [1] RSPM (R 4.0.0)
## base64url 1.4 2018-05-14 [1] RSPM (R 4.0.0)
## bayesplot 1.7.2 2020-05-28 [1] RSPM (R 4.0.0)
## bibtex 0.4.2.2 2020-01-02 [1] RSPM (R 4.0.0)
## Biobase * 2.48.0 2020-04-27 [1] Bioconductor
## BiocGenerics * 0.34.0 2020-04-27 [1] Bioconductor
## BiocManager * 1.30.10 2019-11-16 [1] CRAN (R 4.0.0)
## BiocParallel * 1.22.0 2020-04-27 [1] Bioconductor
## bit 1.1-15.2 2020-02-10 [1] RSPM (R 4.0.0)
## bit64 0.9-7 2017-05-08 [1] RSPM (R 4.0.0)
## bitops 1.0-6 2013-08-17 [1] RSPM (R 4.0.0)
## blob 1.2.1 2020-01-20 [1] RSPM (R 4.0.0)
## boot 1.3-25 2020-04-26 [2] RSPM (R 4.0.0)
## broom * 0.5.6 2020-04-20 [1] RSPM (R 4.0.0)
## callr 3.4.3 2020-03-28 [1] RSPM (R 4.0.0)
## car 3.0-8 2020-05-21 [1] RSPM (R 4.0.0)
## carData 3.0-4 2020-05-22 [1] RSPM (R 4.0.0)
## caret * 6.0-86 2020-03-20 [1] RSPM (R 4.0.0)
## cellranger 1.1.0 2016-07-27 [1] RSPM (R 4.0.0)
## checkmate 2.0.0 2020-02-06 [1] RSPM (R 4.0.0)
## class 7.3-17 2020-04-26 [2] RSPM (R 4.0.0)
## cli 2.0.2 2020-02-28 [1] RSPM (R 4.0.0)
## cluster * 2.1.0 2019-06-19 [1] RSPM (R 4.0.0)
## clusterProfiler * 3.16.1 2020-08-18 [1] Bioconductor
## codetools 0.2-16 2018-12-24 [2] CRAN (R 4.0.0)
## colorspace 1.4-1 2019-03-18 [1] RSPM (R 4.0.0)
## colourpicker 1.0 2017-09-27 [1] RSPM (R 4.0.0)
## corrplot * 0.84 2017-10-16 [1] RSPM (R 4.0.0)
## cowplot * 1.0.0 2019-07-11 [1] RSPM (R 4.0.0)
## crayon 1.3.4 2017-09-16 [1] RSPM (R 4.0.0)
## crosstalk 1.1.0.1 2020-03-13 [1] RSPM (R 4.0.0)
## curl 4.3 2019-12-02 [1] RSPM (R 4.0.0)
## data.table 1.12.8 2019-12-09 [1] RSPM (R 4.0.0)
## DBI 1.1.0 2019-12-15 [1] RSPM (R 4.0.0)
## dbplyr 1.4.4 2020-05-27 [1] RSPM (R 4.0.0)
## DelayedArray * 0.14.1 2020-07-14 [1] Bioconductor
## DESeq2 * 1.28.1 2020-05-12 [1] Bioconductor
## dials * 0.0.6 2020-04-03 [1] RSPM (R 4.0.0)
## DiceDesign 1.8-1 2019-07-31 [1] RSPM (R 4.0.0)
## digest 0.6.25 2020-02-23 [1] RSPM (R 4.0.0)
## DO.db 2.9 2020-09-18 [1] Bioconductor
## doParallel 1.0.15 2019-08-02 [1] RSPM (R 4.0.0)
## DOSE 3.14.0 2020-04-27 [1] Bioconductor
## downloader 0.4 2015-07-09 [1] RSPM (R 4.0.0)
## dplyr * 1.0.0 2020-05-29 [1] RSPM (R 4.0.0)
## drake * 7.12.2 2020-06-02 [1] RSPM (R 4.0.0)
## DT 0.13 2020-03-23 [1] RSPM (R 4.0.0)
## dygraphs 1.1.1.6 2018-07-11 [1] RSPM (R 4.0.0)
## dynamicTreeCut * 1.63-1 2016-03-11 [1] RSPM (R 4.0.0)
## e1071 1.7-3 2019-11-26 [1] RSPM (R 4.0.0)
## edgeR 3.30.3 2020-06-02 [1] Bioconductor
## ellipsis 0.3.1 2020-05-15 [1] RSPM (R 4.0.0)
## enrichplot 1.8.1 2020-04-29 [1] Bioconductor
## europepmc 0.4 2020-05-31 [1] RSPM (R 4.0.0)
## evaluate 0.14 2019-05-28 [1] RSPM (R 4.0.0)
## factoextra * 1.0.7 2020-04-01 [1] RSPM (R 4.0.0)
## fansi 0.4.1 2020-01-08 [1] RSPM (R 4.0.0)
## farver 2.0.3 2020-01-16 [1] RSPM (R 4.0.0)
## fastcluster * 1.1.25 2018-06-07 [1] RSPM (R 4.0.0)
## fastmap 1.0.1 2019-10-08 [1] RSPM (R 4.0.0)
## fastmatch 1.1-0 2017-01-28 [1] RSPM (R 4.0.0)
## fgsea 1.14.0 2020-04-27 [1] Bioconductor
## filelock 1.0.2 2018-10-05 [1] RSPM (R 4.0.0)
## flextable * 0.5.10 2020-05-15 [1] RSPM (R 4.0.0)
## forcats * 0.5.0 2020-03-01 [1] RSPM (R 4.0.0)
## foreach 1.5.0 2020-03-30 [1] RSPM (R 4.0.0)
## foreign 0.8-80 2020-05-24 [2] RSPM (R 4.0.0)
## formattable * 0.2.0.1 2016-08-05 [1] RSPM (R 4.0.0)
## Formula 1.2-3 2018-05-03 [1] RSPM (R 4.0.0)
## fs 1.4.1 2020-04-04 [1] RSPM (R 4.0.0)
## furrr * 0.1.0 2018-05-16 [1] RSPM (R 4.0.0)
## future * 1.17.0 2020-04-18 [1] RSPM (R 4.0.0)
## gdtools 0.2.2 2020-04-03 [1] RSPM (R 4.0.0)
## genefilter * 1.70.0 2020-04-27 [1] Bioconductor
## geneplotter 1.66.0 2020-04-27 [1] Bioconductor
## generics 0.0.2 2018-11-29 [1] RSPM (R 4.0.0)
## GenomeInfoDb * 1.24.2 2020-06-15 [1] Bioconductor
## GenomeInfoDbData 1.2.3 2020-09-18 [1] Bioconductor
## GenomicRanges * 1.40.0 2020-04-27 [1] Bioconductor
## ggforce * 0.3.1 2019-08-20 [1] RSPM (R 4.0.0)
## ggplot2 * 3.3.1 2020-05-28 [1] RSPM (R 4.0.0)
## ggplotify * 0.0.5 2020-03-12 [1] RSPM (R 4.0.0)
## ggpubr * 0.3.0 2020-05-04 [1] RSPM (R 4.0.0)
## ggradar * 0.2 2020-09-18 [1] Github (ricardo-bion/ggradar@63e5cef)
## ggraph 2.0.3 2020-05-20 [1] RSPM (R 4.0.0)
## ggrepel * 0.8.2 2020-03-08 [1] RSPM (R 4.0.0)
## ggridges 0.5.2 2020-01-12 [1] RSPM (R 4.0.0)
## ggsignif 0.6.0 2019-08-08 [1] RSPM (R 4.0.0)
## ggtext * 0.1.0 2020-06-04 [1] RSPM (R 4.0.0)
## globals 0.12.5 2019-12-07 [1] RSPM (R 4.0.0)
## glue 1.4.1 2020-05-13 [1] RSPM (R 4.0.0)
## GO.db 3.11.4 2020-09-18 [1] Bioconductor
## GOSemSim 2.14.2 2020-09-04 [1] Bioconductor
## gower 0.2.1 2019-05-14 [1] RSPM (R 4.0.0)
## GPfit 1.0-8 2019-02-08 [1] RSPM (R 4.0.0)
## graphlayouts 0.7.0 2020-04-25 [1] RSPM (R 4.0.0)
## gridBase 0.4-7 2014-02-24 [1] RSPM (R 4.0.0)
## gridExtra 2.3 2017-09-09 [1] RSPM (R 4.0.0)
## gridGraphics 0.5-0 2020-02-25 [1] RSPM (R 4.0.0)
## gridtext 0.1.1 2020-02-24 [1] RSPM (R 4.0.0)
## gtable 0.3.0 2019-03-25 [1] RSPM (R 4.0.0)
## gtools * 3.8.2 2020-03-31 [1] RSPM (R 4.0.0)
## haven 2.3.1 2020-06-01 [1] RSPM (R 4.0.0)
## HGNChelper * 0.8.1 2019-10-24 [1] RSPM (R 4.0.0)
## Hmisc 4.4-0 2020-03-23 [1] RSPM (R 4.0.0)
## hms 0.5.3 2020-01-08 [1] RSPM (R 4.0.0)
## htmlTable 1.13.3 2019-12-04 [1] RSPM (R 4.0.0)
## htmltools 0.4.0 2019-10-04 [1] RSPM (R 4.0.0)
## htmlwidgets 1.5.1 2019-10-08 [1] RSPM (R 4.0.0)
## httpuv 1.5.4 2020-06-06 [1] RSPM (R 4.0.0)
## httr 1.4.1 2019-08-05 [1] RSPM (R 4.0.0)
## igraph 1.2.5 2020-03-19 [1] RSPM (R 4.0.0)
## import 1.1.0 2015-06-22 [1] RSPM (R 4.0.0)
## impute 1.62.0 2020-04-27 [1] Bioconductor
## infer * 0.5.1 2019-11-19 [1] RSPM (R 4.0.0)
## inline 0.3.15 2018-05-18 [1] RSPM (R 4.0.0)
## ipred 0.9-9 2019-04-28 [1] RSPM (R 4.0.0)
## IRanges * 2.22.2 2020-05-21 [1] Bioconductor
## iterators 1.0.12 2019-07-26 [1] RSPM (R 4.0.0)
## janeaustenr 0.1.5 2017-06-10 [1] RSPM (R 4.0.0)
## janitor * 2.0.1 2020-04-12 [1] RSPM (R 4.0.0)
## jpeg 0.1-8.1 2019-10-24 [1] RSPM (R 4.0.0)
## jsonlite 1.6.1 2020-02-02 [1] RSPM (R 4.0.0)
## kableExtra * 1.1.0 2019-03-16 [1] RSPM (R 4.0.0)
## knitr * 1.28 2020-02-06 [1] RSPM (R 4.0.0)
## labeling 0.3 2014-08-23 [1] RSPM (R 4.0.0)
## later 1.1.0.1 2020-06-05 [1] RSPM (R 4.0.0)
## lattice * 0.20-41 2020-04-02 [2] CRAN (R 4.0.0)
## latticeExtra 0.6-29 2019-12-19 [1] RSPM (R 4.0.0)
## lava 1.6.7 2020-03-05 [1] RSPM (R 4.0.0)
## lazyeval 0.2.2 2019-03-15 [1] RSPM (R 4.0.0)
## lhs 1.0.2 2020-04-13 [1] RSPM (R 4.0.0)
## lifecycle 0.2.0 2020-03-06 [1] RSPM (R 4.0.0)
## limma 3.44.3 2020-06-12 [1] Bioconductor
## listenv 0.8.0 2019-12-05 [1] RSPM (R 4.0.0)
## lme4 1.1-23 2020-04-07 [1] RSPM (R 4.0.0)
## locfit 1.5-9.4 2020-03-25 [1] RSPM (R 4.0.0)
## loo 2.2.0 2019-12-19 [1] RSPM (R 4.0.0)
## lubridate 1.7.8 2020-04-06 [1] RSPM (R 4.0.0)
## magrittr 1.5 2014-11-22 [1] RSPM (R 4.0.0)
## markdown 1.1 2019-08-07 [1] RSPM (R 4.0.0)
## MASS 7.3-51.6 2020-04-26 [2] RSPM (R 4.0.0)
## Matrix * 1.2-18 2019-11-27 [2] CRAN (R 4.0.0)
## matrixStats * 0.56.0 2020-03-13 [1] RSPM (R 4.0.0)
## memoise 1.1.0 2017-04-21 [1] RSPM (R 4.0.0)
## mgcv * 1.8-31 2019-11-09 [2] CRAN (R 4.0.0)
## mime 0.9 2020-02-04 [1] RSPM (R 4.0.0)
## miniUI 0.1.1.1 2018-05-18 [1] RSPM (R 4.0.0)
## minqa 1.2.4 2014-10-09 [1] RSPM (R 4.0.0)
## ModelMetrics 1.2.2.2 2020-03-17 [1] RSPM (R 4.0.0)
## modelr 0.1.8 2020-05-19 [1] RSPM (R 4.0.0)
## moduleScoreR * 0.0.0.9200 2020-09-18 [1] Github (milescsmith/moduleScoreR@e6db70f)
## munsell 0.5.0 2018-06-12 [1] RSPM (R 4.0.0)
## nlme * 3.1-148 2020-05-24 [2] RSPM (R 4.0.0)
## nloptr 1.2.2.1 2020-03-11 [1] RSPM (R 4.0.0)
## NMF 0.22.0 2020-02-12 [1] RSPM (R 4.0.0)
## nnet 7.3-14 2020-04-26 [2] RSPM (R 4.0.0)
## officer 0.3.11 2020-05-18 [1] RSPM (R 4.0.0)
## openxlsx 4.1.5 2020-05-06 [1] RSPM (R 4.0.0)
## paletteer * 1.2.0 2020-06-07 [1] RSPM (R 4.0.0)
## parsnip * 0.1.1 2020-05-06 [1] RSPM (R 4.0.0)
## pheatmap * 1.0.12 2019-01-04 [1] RSPM (R 4.0.0)
## pillar 1.4.4 2020-05-05 [1] RSPM (R 4.0.0)
## pkgbuild 1.0.8 2020-05-07 [1] RSPM (R 4.0.0)
## pkgconfig 2.0.3 2019-09-22 [1] RSPM (R 4.0.0)
## pkgmaker 0.31.1 2020-03-19 [1] RSPM (R 4.0.0)
## plotly * 4.9.2.1 2020-04-04 [1] RSPM (R 4.0.0)
## plyr 1.8.6 2020-03-03 [1] RSPM (R 4.0.0)
## png 0.1-7 2013-12-03 [1] RSPM (R 4.0.0)
## polyclip 1.10-0 2019-03-14 [1] RSPM (R 4.0.0)
## preprocessCore 1.50.0 2020-04-27 [1] Bioconductor
## prettyunits 1.1.1 2020-01-24 [1] RSPM (R 4.0.0)
## prismatic 0.2.0 2019-12-01 [1] RSPM (R 4.0.0)
## pROC 1.16.2 2020-03-19 [1] RSPM (R 4.0.0)
## processx 3.4.2 2020-02-09 [1] RSPM (R 4.0.0)
## prodlim 2019.11.13 2019-11-17 [1] RSPM (R 4.0.0)
## progress 1.2.2 2019-05-16 [1] RSPM (R 4.0.0)
## promises 1.1.0 2019-10-04 [1] RSPM (R 4.0.0)
## ps 1.3.3 2020-05-08 [1] RSPM (R 4.0.0)
## purrr * 0.3.4 2020-04-17 [1] RSPM (R 4.0.0)
## qvalue 2.20.0 2020-04-27 [1] Bioconductor
## R6 2.4.1 2019-11-12 [1] RSPM (R 4.0.0)
## randomForest * 4.6-14 2018-03-25 [1] RSPM (R 4.0.0)
## RColorBrewer * 1.1-2 2014-12-07 [1] RSPM (R 4.0.0)
## Rcpp 1.0.4.6 2020-04-09 [1] RSPM (R 4.0.0)
## RCurl 1.98-1.2 2020-04-18 [1] RSPM (R 4.0.0)
## readr * 1.3.1 2018-12-21 [1] RSPM (R 4.0.0)
## readxl * 1.3.1 2019-03-13 [1] RSPM (R 4.0.0)
## recipes * 0.1.12 2020-05-01 [1] RSPM (R 4.0.0)
## registry 0.5-1 2019-03-05 [1] RSPM (R 4.0.0)
## rematch2 2.1.2 2020-05-01 [1] RSPM (R 4.0.0)
## reprex 0.3.0 2019-05-16 [1] RSPM (R 4.0.0)
## reshape2 1.4.4 2020-04-09 [1] RSPM (R 4.0.0)
## rio 0.5.16 2018-11-26 [1] RSPM (R 4.0.0)
## rlang * 0.4.6 2020-05-02 [1] RSPM (R 4.0.0)
## rmarkdown 2.2 2020-05-31 [1] RSPM (R 4.0.0)
## rngtools 1.5 2020-01-23 [1] RSPM (R 4.0.0)
## rpart 4.1-15 2019-04-12 [2] CRAN (R 4.0.0)
## rsample * 0.0.7 2020-06-04 [1] RSPM (R 4.0.0)
## rsconnect 0.8.16 2019-12-13 [1] RSPM (R 4.0.0)
## RSQLite 2.2.0 2020-01-07 [1] RSPM (R 4.0.0)
## rstan 2.19.3 2020-02-11 [1] RSPM (R 4.0.0)
## rstanarm 2.19.3 2020-02-11 [1] RSPM (R 4.0.0)
## rstantools 2.1.0 2020-06-01 [1] RSPM (R 4.0.0)
## rstatix * 0.6.0.999 2020-09-18 [1] Github (milescsmith/rstatix@6f5e0a6)
## rstudioapi 0.11 2020-02-07 [1] RSPM (R 4.0.0)
## rsvd 1.0.3 2020-02-17 [1] RSPM (R 4.0.0)
## rvcheck 0.1.8 2020-03-01 [1] RSPM (R 4.0.0)
## rvest 0.3.5 2019-11-08 [1] RSPM (R 4.0.0)
## S4Vectors * 0.26.1 2020-05-16 [1] Bioconductor
## scales * 1.1.1 2020-05-11 [1] RSPM (R 4.0.0)
## scatterpie 0.1.4 2019-11-08 [1] RSPM (R 4.0.0)
## sessioninfo 1.1.1 2018-11-05 [1] RSPM (R 4.0.0)
## shiny 1.4.0.2 2020-03-13 [1] RSPM (R 4.0.0)
## shinyjs 1.1 2020-01-13 [1] RSPM (R 4.0.0)
## shinystan 2.5.0 2018-05-01 [1] RSPM (R 4.0.0)
## shinythemes 1.1.2 2018-11-06 [1] RSPM (R 4.0.0)
## snakecase 0.11.0 2019-05-25 [1] RSPM (R 4.0.0)
## snow 0.4-3 2018-09-14 [1] RSPM (R 4.0.0)
## SnowballC 0.7.0 2020-04-01 [1] RSPM (R 4.0.0)
## StanHeaders 2.21.0-3 2020-05-28 [1] RSPM (R 4.0.0)
## statmod 1.4.34 2020-02-17 [1] RSPM (R 4.0.0)
## storr 1.2.1 2018-10-18 [1] RSPM (R 4.0.0)
## stringi 1.4.6 2020-02-17 [1] RSPM (R 4.0.0)
## stringr * 1.4.0 2019-02-10 [1] RSPM (R 4.0.0)
## SummarizedExperiment * 1.18.2 2020-07-09 [1] Bioconductor
## survival 3.1-12 2020-04-10 [2] CRAN (R 4.0.0)
## sva * 3.36.0 2020-04-27 [1] Bioconductor
## systemfonts 0.2.2 2020-05-14 [1] RSPM (R 4.0.0)
## threejs 0.3.3 2020-01-21 [1] RSPM (R 4.0.0)
## tibble * 3.0.1 2020-04-20 [1] RSPM (R 4.0.0)
## tidygraph 1.2.0 2020-05-12 [1] RSPM (R 4.0.0)
## tidymodels * 0.1.0 2020-02-16 [1] RSPM (R 4.0.0)
## tidyposterior 0.0.2 2018-11-15 [1] RSPM (R 4.0.0)
## tidypredict 0.4.5 2020-02-10 [1] RSPM (R 4.0.0)
## tidyr * 1.1.0 2020-05-20 [1] RSPM (R 4.0.0)
## tidyselect * 1.1.0 2020-05-11 [1] RSPM (R 4.0.0)
## tidytext 0.2.4 2020-04-17 [1] RSPM (R 4.0.0)
## tidyverse * 1.3.0 2019-11-21 [1] RSPM (R 4.0.0)
## timeDate 3043.102 2018-02-21 [1] RSPM (R 4.0.0)
## tokenizers 0.2.1 2018-03-29 [1] RSPM (R 4.0.0)
## triebeard 0.3.0 2016-08-04 [1] RSPM (R 4.0.0)
## tune * 0.1.0 2020-04-02 [1] RSPM (R 4.0.0)
## tweenr 1.0.1 2018-12-14 [1] RSPM (R 4.0.0)
## tximport * 1.16.1 2020-06-05 [1] Bioconductor
## txtq 0.2.0 2019-10-15 [1] RSPM (R 4.0.0)
## urltools 1.7.3 2019-04-14 [1] RSPM (R 4.0.0)
## uuid 0.1-4 2020-02-26 [1] RSPM (R 4.0.0)
## uwot * 0.1.8 2020-03-16 [1] RSPM (R 4.0.0)
## vctrs 0.3.1 2020-06-05 [1] RSPM (R 4.0.0)
## viridis * 0.5.1 2018-03-29 [1] RSPM (R 4.0.0)
## viridisLite * 0.3.0 2018-02-01 [1] RSPM (R 4.0.0)
## webshot 0.5.2 2019-11-22 [1] RSPM (R 4.0.0)
## WGCNA * 1.69 2020-02-28 [1] RSPM (R 4.0.0)
## withr 2.2.0 2020-04-20 [1] RSPM (R 4.0.0)
## workflows * 0.1.1 2020-03-17 [1] RSPM (R 4.0.0)
## xfun 0.14 2020-05-20 [1] RSPM (R 4.0.0)
## XML 3.99-0.3 2020-01-20 [1] RSPM (R 4.0.0)
## xml2 1.3.2 2020-04-23 [1] RSPM (R 4.0.0)
## xtable 1.8-4 2019-04-21 [1] RSPM (R 4.0.0)
## xts 0.12-0 2020-01-19 [1] RSPM (R 4.0.0)
## XVector 0.28.0 2020-04-27 [1] Bioconductor
## yaml 2.2.1 2020-02-01 [1] RSPM (R 4.0.0)
## yardstick * 0.0.6 2020-03-17 [1] RSPM (R 4.0.0)
## zip 2.0.4 2019-09-01 [1] RSPM (R 4.0.0)
## zlibbioc 1.34.0 2020-04-27 [1] Bioconductor
## zoo 1.8-8 2020-05-02 [1] RSPM (R 4.0.0)
##
## [1] /usr/local/lib/R/site-library
## [2] /usr/local/lib/R/library